PyTest - Mocking

#pytest   #mock   #unittest  

---

Table of Content

PyTest - Mocking

mocking

A mock object is a simulated object that mimics the behavior of the smallest testable parts of an application in controlled ways. It’s replace of one or more function or objects calls

A mock function call return a predefined value immediately without doing any work

In Python mocking implement by unittest.mock module

demo1

How To Mock Patch A Function

demo.py

from random import randint

def func_under_test():
    r = randint(1, 8)
    return r

test_demo.py

from demo import func_under_test
from unittest import mock

@mock.patch("demo.randint", return_value=7, autospec=True)
def test_with_mock(mock_randint):
    r = func_under_test()
    assert r == 7

patch

Patch the thing where it used not where it’s import In the above example we patch demo.randint and not random.randint

autospec

autospec

---

Simple demo

project

search
  ├── tutorial
  │ ├── __init__.py
  │ └── demo.py
  └── tests
   └── test_demo.py

demo.py

# method to mock
def get_number() -> int:
    return 5

# function under test
def add(a: int) -> int:
    b = get_number()
    return a + b

test_demo.py

from unittest.mock import patch, MagicMock

@patch("tutorial.demo.get_number")
def test_add_mock(mock_get_number: MagicMock) -> None:
    mock_get_number.return_value = 2
    result = add(1)
    assert result == 3

Warning

@path full name of the function or class to patch module_name.func_name for example to path get_number function in demo module. @patch("demo.get_number")

---

Mock with parametrize

• Set mock return_value from parameter

demo.py

from random import randint

def get_random():
    return randint(1, 10)


def good_random():
    r = get_random()
    if r > 7:
        return "win"
    else:
        return "lose"

test_demo.py

import pytest
from unittest import mock
from demo import good_random


@pytest.mark.parametrize("_input, expected", [(8, "win"), (5, "lose")])
@mock.patch("demo.get_random")
def test_good_random(mock_get_random, _input, expected):
    mock_get_random.return_value = _input
    result = good_random()
    assert result == expected

---

MagicMock

Provide a simple mocking interface that allow to mock partial real object that we wont to patch

return_value

allows you to choose what the patched callable returns, usually we return the same type of the real callable but controllable

side_effect

Change the behavior of the mock

side_effect = Iterable

yield the values from defined iterable on subsequent call

>>> from unittest.mock import MagicMock
>>> m = MagicMock()
>>> m.get_data.side_effect = [5, 10, 15]
>>> m.get_data()
5
>>> m.get_data()
10
>>> m.get_data()
15

from unittest.mock import patch

def my_input() -> int:
    return 1

def method_to_test():
    a = my_input()
    b = my_input()
    return a+b


@patch("test_demo.my_input")
def test_multiple(mock_my_input):
    mock_my_input.side_effect = [1, 2]
    result = method_to_test()
    assert result == 3

side_effect = Exception

m.check.side_effect = Exception("custom exception")
>>> m.check()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/python3.8/unittest/mock.py", 
  ...
    raise effect
Exception: custom exception

ide_effect = callable

The callable will be executed on each call with the parameters passed when calling the mocked method

>>> def call_me(name):
...     print(name)
... 
>>> m.run_call.side_effect = call_me
>>> m.run_call("a")
a
>>> m.run_call("b")
b
>>> m.run_call.call_count
2
>>> m.run_call("b")
b
>>> m.run_call.call_count
3

---

Reference

• Mock’s return_value & side effect
• Unit Testing in Python with pytest | Introduction to mock (Part-9)
• Unit Testing in Python with pytest | Advanced Mocking (Part-10)